Overview

Dataset statistics

Number of variables13
Number of observations1991
Missing cells992
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory202.3 KiB
Average record size in memory104.1 B

Variable types

Numeric10
Categorical3

Alerts

LOCATIONS has a high cardinality: 666 distinct valuesHigh cardinality
STATE has a high cardinality: 203 distinct valuesHigh cardinality
year is highly overall correlated with PotabilityHigh correlation
Potability is highly overall correlated with yearHigh correlation
STATION CODE has 122 (6.1%) missing valuesMissing
ph has 301 (15.1%) missing valuesMissing
Sulfate has 469 (23.6%) missing valuesMissing
Trihalomethanes has 100 (5.0%) missing valuesMissing
Hardness has unique valuesUnique
Solids has unique valuesUnique
Chloramines has unique valuesUnique
Organic_carbon has unique valuesUnique
Turbidity has unique valuesUnique

Reproduction

Analysis started2023-05-15 09:54:37.382604
Analysis finished2023-05-15 09:55:01.843360
Duration24.46 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

STATION CODE
Real number (ℝ)

Distinct320
Distinct (%)17.1%
Missing122
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean1953.6447
Minimum2
Maximum3473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:01.990571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1026
Q11448
median1861
Q32423
95-th percentile3362
Maximum3473
Range3471
Interquartile range (IQR)975

Descriptive statistics

Standard deviation744.95769
Coefficient of variation (CV)0.38131687
Kurtosis0.045686455
Mean1953.6447
Median Absolute Deviation (MAD)446
Skewness0.014082885
Sum3651362
Variance554961.96
MonotonicityNot monotonic
2023-05-15T15:25:02.131161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1573 10
 
0.5%
1571 10
 
0.5%
1643 10
 
0.5%
42 10
 
0.5%
1450 10
 
0.5%
1399 10
 
0.5%
1566 10
 
0.5%
1564 10
 
0.5%
1159 10
 
0.5%
1151 10
 
0.5%
Other values (310) 1769
88.8%
(Missing) 122
 
6.1%
ValueCountFrequency (%)
2 1
 
0.1%
17 10
0.5%
18 10
0.5%
20 10
0.5%
21 10
0.5%
42 10
0.5%
43 10
0.5%
1023 9
0.5%
1024 9
0.5%
1025 9
0.5%
ValueCountFrequency (%)
3473 3
0.2%
3471 3
0.2%
3468 3
0.2%
3466 3
0.2%
3465 3
0.2%
3464 3
0.2%
3460 3
0.2%
3459 3
0.2%
3458 3
0.2%
3384 3
0.2%

LOCATIONS
Categorical

Distinct666
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
NAN
184 
GAUTAMINANGODAVARI RIVER
 
10
ZUARI AT PANCHAWADI
 
10
PERIYAR AT SEWAGE DISCHARGE POINT, KERALA
 
8
TUIRIAL LOWER CATCHMENT
 
8
Other values (661)
1771 

Length

Max length110
Median length77
Mean length33.595178
Min length3

Characters and Unicode

Total characters66888
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)15.3%

Sample

1st rowDAMANGANGA AT D/S OF MADHUBAN, DAMAN
2nd rowZUARI AT D/S OF PT. WHERE KUMBARJRIA CANAL JOINS, GOA
3rd rowZUARI AT PANCHAWADI
4th rowRIVER ZUARI AT BORIM BRIDGE
5th rowRIVER ZUARI AT MARCAIM JETTY

Common Values

ValueCountFrequency (%)
NAN 184
 
9.2%
GAUTAMINANGODAVARI RIVER 10
 
0.5%
ZUARI AT PANCHAWADI 10
 
0.5%
PERIYAR AT SEWAGE DISCHARGE POINT, KERALA 8
 
0.4%
TUIRIAL LOWER CATCHMENT 8
 
0.4%
KALU AT ATALE VILLAGE, MAHARASHTRA 8
 
0.4%
TUIRIAL UPPER CATCHMENT 8
 
0.4%
KUNDALIKA AT ROHA CITY, MAHARASHTRA 8
 
0.4%
TLAWNG DOWNSTREAM AIZAWL 8
 
0.4%
TLAWNG UPSTREAM AIZAWL 8
 
0.4%
Other values (656) 1731
86.9%

Length

2023-05-15T15:25:02.308690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
at 1353
 
13.6%
river 535
 
5.4%
of 243
 
2.4%
d/s 222
 
2.2%
nan 221
 
2.2%
kerala 211
 
2.1%
r 203
 
2.0%
bridge 135
 
1.4%
near 128
 
1.3%
u/s 123
 
1.2%
Other values (765) 6611
66.2%

Most occurring characters

ValueCountFrequency (%)
A 11551
17.3%
8079
 
12.1%
R 5121
 
7.7%
I 3859
 
5.8%
N 3640
 
5.4%
T 3607
 
5.4%
E 2818
 
4.2%
L 2802
 
4.2%
H 2368
 
3.5%
U 2152
 
3.2%
Other values (42) 20891
31.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56118
83.9%
Space Separator 8079
 
12.1%
Other Punctuation 2309
 
3.5%
Open Punctuation 135
 
0.2%
Close Punctuation 129
 
0.2%
Decimal Number 96
 
0.1%
Lowercase Letter 22
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11551
20.6%
R 5121
 
9.1%
I 3859
 
6.9%
N 3640
 
6.5%
T 3607
 
6.4%
E 2818
 
5.0%
L 2802
 
5.0%
H 2368
 
4.2%
U 2152
 
3.8%
M 2102
 
3.7%
Other values (16) 16098
28.7%
Lowercase Letter
ValueCountFrequency (%)
a 5
22.7%
e 3
13.6%
r 3
13.6%
s 3
13.6%
t 2
 
9.1%
o 1
 
4.5%
f 1
 
4.5%
u 1
 
4.5%
v 1
 
4.5%
g 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 24
25.0%
2 19
19.8%
0 18
18.8%
3 13
13.5%
8 9
 
9.4%
6 6
 
6.2%
9 6
 
6.2%
5 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 1535
66.5%
/ 395
 
17.1%
. 364
 
15.8%
\ 15
 
0.6%
Space Separator
ValueCountFrequency (%)
8079
100.0%
Open Punctuation
ValueCountFrequency (%)
( 135
100.0%
Close Punctuation
ValueCountFrequency (%)
) 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56140
83.9%
Common 10748
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11551
20.6%
R 5121
 
9.1%
I 3859
 
6.9%
N 3640
 
6.5%
T 3607
 
6.4%
E 2818
 
5.0%
L 2802
 
5.0%
H 2368
 
4.2%
U 2152
 
3.8%
M 2102
 
3.7%
Other values (27) 16120
28.7%
Common
ValueCountFrequency (%)
8079
75.2%
, 1535
 
14.3%
/ 395
 
3.7%
. 364
 
3.4%
( 135
 
1.3%
) 129
 
1.2%
1 24
 
0.2%
2 19
 
0.2%
0 18
 
0.2%
\ 15
 
0.1%
Other values (5) 35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11551
17.3%
8079
 
12.1%
R 5121
 
7.7%
I 3859
 
5.8%
N 3640
 
5.4%
T 3607
 
5.4%
E 2818
 
4.2%
L 2802
 
4.2%
H 2368
 
3.5%
U 2152
 
3.2%
Other values (42) 20891
31.2%

STATE
Categorical

Distinct203
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
NAN
761 
KERALA
275 
MAHARASHTRA
142 
MEGHALAYA
125 
GOA
101 
Other values (198)
587 

Length

Max length93
Median length79
Mean length8.4590658
Min length3

Characters and Unicode

Total characters16842
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)8.9%

Sample

1st rowDAMAN & DIU
2nd rowGOA
3rd rowGOA
4th rowGOA
5th rowGOA

Common Values

ValueCountFrequency (%)
NAN 761
38.2%
KERALA 275
 
13.8%
MAHARASHTRA 142
 
7.1%
MEGHALAYA 125
 
6.3%
GOA 101
 
5.1%
MANIPUR 76
 
3.8%
PUNJAB 48
 
2.4%
TAMILNADU 42
 
2.1%
GUJARAT 37
 
1.9%
ORISSA 30
 
1.5%
Other values (193) 354
17.8%

Length

2023-05-15T15:25:02.464948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan 765
27.1%
kerala 303
 
10.7%
maharashtra 149
 
5.3%
meghalaya 129
 
4.6%
at 124
 
4.4%
goa 110
 
3.9%
manipur 84
 
3.0%
punjab 58
 
2.1%
tamilnadu 46
 
1.6%
gujarat 39
 
1.4%
Other values (429) 1020
36.1%

Most occurring characters

ValueCountFrequency (%)
A 4212
25.0%
N 2127
12.6%
R 1418
 
8.4%
842
 
5.0%
H 775
 
4.6%
E 729
 
4.3%
L 728
 
4.3%
M 682
 
4.0%
T 600
 
3.6%
I 591
 
3.5%
Other values (39) 4138
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15676
93.1%
Space Separator 842
 
5.0%
Other Punctuation 248
 
1.5%
Lowercase Letter 42
 
0.2%
Close Punctuation 14
 
0.1%
Open Punctuation 14
 
0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4212
26.9%
N 2127
13.6%
R 1418
 
9.0%
H 775
 
4.9%
E 729
 
4.7%
L 728
 
4.6%
M 682
 
4.4%
T 600
 
3.8%
I 591
 
3.8%
G 462
 
2.9%
Other values (15) 3352
21.4%
Lowercase Letter
ValueCountFrequency (%)
a 10
23.8%
r 8
19.0%
p 4
 
9.5%
h 3
 
7.1%
u 3
 
7.1%
t 3
 
7.1%
i 3
 
7.1%
s 2
 
4.8%
d 2
 
4.8%
n 2
 
4.8%
Other values (2) 2
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 155
62.5%
. 40
 
16.1%
/ 39
 
15.7%
& 12
 
4.8%
\ 2
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
0 2
33.3%
2 1
16.7%
8 1
16.7%
Space Separator
ValueCountFrequency (%)
842
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15718
93.3%
Common 1124
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4212
26.8%
N 2127
13.5%
R 1418
 
9.0%
H 775
 
4.9%
E 729
 
4.6%
L 728
 
4.6%
M 682
 
4.3%
T 600
 
3.8%
I 591
 
3.8%
G 462
 
2.9%
Other values (27) 3394
21.6%
Common
ValueCountFrequency (%)
842
74.9%
, 155
 
13.8%
. 40
 
3.6%
/ 39
 
3.5%
) 14
 
1.2%
( 14
 
1.2%
& 12
 
1.1%
1 2
 
0.2%
0 2
 
0.2%
\ 2
 
0.2%
Other values (2) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4212
25.0%
N 2127
12.6%
R 1418
 
8.4%
842
 
5.0%
H 775
 
4.6%
E 729
 
4.3%
L 728
 
4.3%
M 682
 
4.0%
T 600
 
3.6%
I 591
 
3.5%
Other values (39) 4138
24.6%

year
Real number (ℝ)

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.0382
Minimum2003
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:02.589875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2005
Q12008
median2011
Q32013
95-th percentile2014
Maximum2014
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0573331
Coefficient of variation (CV)0.0015210324
Kurtosis-0.57394271
Mean2010.0382
Median Absolute Deviation (MAD)2
Skewness-0.59388594
Sum4001986
Variance9.3472859
MonotonicityDecreasing
2023-05-15T15:25:02.683607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2012 292
14.7%
2013 261
13.1%
2014 245
12.3%
2011 231
11.6%
2010 188
9.4%
2009 181
9.1%
2008 159
8.0%
2007 120
6.0%
2005 119
6.0%
2006 105
 
5.3%
Other values (2) 90
 
4.5%
ValueCountFrequency (%)
2003 88
 
4.4%
2004 2
 
0.1%
2005 119
6.0%
2006 105
 
5.3%
2007 120
6.0%
2008 159
8.0%
2009 181
9.1%
2010 188
9.4%
2011 231
11.6%
2012 292
14.7%
ValueCountFrequency (%)
2014 245
12.3%
2013 261
13.1%
2012 292
14.7%
2011 231
11.6%
2010 188
9.4%
2009 181
9.1%
2008 159
8.0%
2007 120
6.0%
2006 105
 
5.3%
2005 119
6.0%

ph
Real number (ℝ)

Distinct1690
Distinct (%)100.0%
Missing301
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean7.0850939
Minimum0.22749905
Maximum13.175402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:02.828557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.22749905
5-th percentile4.6936904
Q16.145172
median7.0323289
Q38.0072492
95-th percentile9.6427091
Maximum13.175402
Range12.947903
Interquartile range (IQR)1.8620772

Descriptive statistics

Standard deviation1.5091002
Coefficient of variation (CV)0.2129965
Kurtosis0.61768791
Mean7.0850939
Median Absolute Deviation (MAD)0.92829066
Skewness0.0055431689
Sum11973.809
Variance2.2773834
MonotonicityNot monotonic
2023-05-15T15:25:02.998379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.909640554 1
 
0.1%
6.652824348 1
 
0.1%
8.070477127 1
 
0.1%
6.584813111 1
 
0.1%
5.742533063 1
 
0.1%
5.343075103 1
 
0.1%
6.057068041 1
 
0.1%
8.132736965 1
 
0.1%
7.021295306 1
 
0.1%
5.918953546 1
 
0.1%
Other values (1680) 1680
84.4%
(Missing) 301
 
15.1%
ValueCountFrequency (%)
0.2274990502 1
0.1%
0.9899122129 1
0.1%
1.757037115 1
0.1%
1.844538366 1
0.1%
2.569243562 1
0.1%
2.612035915 1
0.1%
2.69083124 1
0.1%
2.798549099 1
0.1%
3.344588533 1
0.1%
3.388090611 1
0.1%
ValueCountFrequency (%)
13.17540172 1
0.1%
12.24692807 1
0.1%
11.89807803 1
0.1%
11.53488049 1
0.1%
11.301794 1
0.1%
11.26782838 1
0.1%
11.24450714 1
0.1%
11.18069466 1
0.1%
11.18028447 1
0.1%
11.02787986 1
0.1%

Hardness
Real number (ℝ)

Distinct1991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.64565
Minimum47.432
Maximum323.124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:03.138930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum47.432
5-th percentile144.95655
Q1177.22372
median197.46047
Q3215.78655
95-th percentile248.84627
Maximum323.124
Range275.692
Interquartile range (IQR)38.562829

Descriptive statistics

Standard deviation32.211865
Coefficient of variation (CV)0.16380665
Kurtosis0.99974932
Mean196.64565
Median Absolute Deviation (MAD)19.22681
Skewness-0.02916238
Sum391521.49
Variance1037.6042
MonotonicityNot monotonic
2023-05-15T15:25:03.279526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204.8904555 1
 
0.1%
196.7827837 1
 
0.1%
198.8659477 1
 
0.1%
182.3754558 1
 
0.1%
182.941032 1
 
0.1%
152.5076794 1
 
0.1%
211.6620911 1
 
0.1%
184.3732318 1
 
0.1%
206.786448 1
 
0.1%
189.814682 1
 
0.1%
Other values (1981) 1981
99.5%
ValueCountFrequency (%)
47.432 1
0.1%
73.49223369 1
0.1%
77.4595861 1
0.1%
81.71089527 1
0.1%
94.09130748 1
0.1%
97.2809086 1
0.1%
98.45293051 1
0.1%
98.77164353 1
0.1%
100.4576151 1
0.1%
103.173587 1
0.1%
ValueCountFrequency (%)
323.124 1
0.1%
311.3839565 1
0.1%
308.2538329 1
0.1%
307.7060241 1
0.1%
306.6274814 1
0.1%
304.2359121 1
0.1%
300.2924758 1
0.1%
291.4618974 1
0.1%
287.3702082 1
0.1%
286.2017633 1
0.1%

Solids
Real number (ℝ)

Distinct1991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22033.523
Minimum1372.091
Maximum56867.859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:03.484747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1372.091
5-th percentile9194.5327
Q115472.588
median20920.252
Q327419.474
95-th percentile38873.693
Maximum56867.859
Range55495.768
Interquartile range (IQR)11946.886

Descriptive statistics

Standard deviation8951.8752
Coefficient of variation (CV)0.40628434
Kurtosis0.26585929
Mean22033.523
Median Absolute Deviation (MAD)5948.3025
Skewness0.59887461
Sum43868743
Variance80136069
MonotonicityNot monotonic
2023-05-15T15:25:03.687825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20791.31898 1
 
0.1%
19024.68867 1
 
0.1%
18266.61772 1
 
0.1%
24723.1063 1
 
0.1%
21293.88975 1
 
0.1%
11398.7311 1
 
0.1%
45166.91214 1
 
0.1%
14807.26849 1
 
0.1%
25838.12848 1
 
0.1%
19887.76983 1
 
0.1%
Other values (1981) 1981
99.5%
ValueCountFrequency (%)
1372.091043 1
0.1%
2552.962804 1
0.1%
2808.025756 1
0.1%
2912.211247 1
0.1%
3773.281147 1
0.1%
3802.411681 1
0.1%
3900.913892 1
0.1%
4111.785432 1
0.1%
4142.499001 1
0.1%
4168.196994 1
0.1%
ValueCountFrequency (%)
56867.85924 1
0.1%
56488.67241 1
0.1%
56351.3963 1
0.1%
55334.7028 1
0.1%
52318.9173 1
0.1%
52060.2268 1
0.1%
50279.26243 1
0.1%
49125.36008 1
0.1%
49074.73041 1
0.1%
49009.92466 1
0.1%

Chloramines
Real number (ℝ)

Distinct1991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0891025
Minimum0.53035129
Maximum13.127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:03.940545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.53035129
5-th percentile4.4980086
Q16.0330555
median7.0891462
Q38.122656
95-th percentile9.7641521
Maximum13.127
Range12.596649
Interquartile range (IQR)2.0896005

Descriptive statistics

Standard deviation1.5978603
Coefficient of variation (CV)0.2253967
Kurtosis0.28544938
Mean7.0891025
Median Absolute Deviation (MAD)1.048411
Skewness0.057570783
Sum14114.403
Variance2.5531576
MonotonicityNot monotonic
2023-05-15T15:25:04.143623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.300211873 1
 
0.1%
6.911867556 1
 
0.1%
6.90236983 1
 
0.1%
6.238919727 1
 
0.1%
6.826412756 1
 
0.1%
8.728973148 1
 
0.1%
6.651801292 1
 
0.1%
5.753405052 1
 
0.1%
8.684832598 1
 
0.1%
8.115767881 1
 
0.1%
Other values (1981) 1981
99.5%
ValueCountFrequency (%)
0.5303512947 1
0.1%
1.683992581 1
0.1%
2.456013596 1
0.1%
2.484379977 1
0.1%
2.577555273 1
0.1%
2.621267556 1
0.1%
2.741712117 1
0.1%
2.750837309 1
0.1%
2.862535374 1
0.1%
2.86607303 1
0.1%
ValueCountFrequency (%)
13.127 1
0.1%
13.04380611 1
0.1%
12.91218664 1
0.1%
12.58002649 1
0.1%
12.36328483 1
0.1%
12.27937418 1
0.1%
12.0625362 1
0.1%
11.58615108 1
0.1%
11.54319047 1
0.1%
11.52359751 1
0.1%

Sulfate
Real number (ℝ)

Distinct1522
Distinct (%)100.0%
Missing469
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean332.24427
Minimum129
Maximum476.53972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:04.331078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile261.21604
Q1306.26073
median331.53619
Q3358.24268
95-th percentile400.69067
Maximum476.53972
Range347.53972
Interquartile range (IQR)51.981954

Descriptive statistics

Standard deviation42.539915
Coefficient of variation (CV)0.12803807
Kurtosis0.84516993
Mean332.24427
Median Absolute Deviation (MAD)25.820545
Skewness-0.092040178
Sum505675.77
Variance1809.6444
MonotonicityNot monotonic
2023-05-15T15:25:04.551961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
376.811708 1
 
0.1%
396.6195096 1
 
0.1%
306.5430715 1
 
0.1%
400.6696015 1
 
0.1%
279.7674997 1
 
0.1%
384.8219665 1
 
0.1%
346.6118493 1
 
0.1%
415.9278983 1
 
0.1%
320.535989 1
 
0.1%
321.9745697 1
 
0.1%
Other values (1512) 1512
75.9%
(Missing) 469
 
23.6%
ValueCountFrequency (%)
129 1
0.1%
180.2067464 1
0.1%
182.3973702 1
0.1%
187.1707144 1
0.1%
187.4241309 1
0.1%
192.0335917 1
0.1%
203.4445208 1
0.1%
206.2472294 1
0.1%
207.8904823 1
0.1%
209.4710584 1
0.1%
ValueCountFrequency (%)
476.5397173 1
0.1%
475.7374602 1
0.1%
462.474215 1
0.1%
460.107069 1
0.1%
458.4410723 1
0.1%
455.4512337 1
0.1%
449.2676875 1
0.1%
445.9383912 1
0.1%
445.3595467 1
0.1%
444.970552 1
0.1%

Organic_carbon
Real number (ℝ)

Distinct1991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.242741
Minimum2.2
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:04.692587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.8142365
Q112.001863
median14.189062
Q316.543395
95-th percentile19.585249
Maximum28.3
Range26.1
Interquartile range (IQR)4.5415326

Descriptive statistics

Standard deviation3.3167334
Coefficient of variation (CV)0.23287185
Kurtosis0.056122114
Mean14.242741
Median Absolute Deviation (MAD)2.2717747
Skewness-0.0030376768
Sum28357.298
Variance11.000721
MonotonicityNot monotonic
2023-05-15T15:25:04.848770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.37978308 1
 
0.1%
10.77286174 1
 
0.1%
10.92446056 1
 
0.1%
17.58261495 1
 
0.1%
11.14407224 1
 
0.1%
11.67660134 1
 
0.1%
19.68233697 1
 
0.1%
14.7530554 1
 
0.1%
11.63301523 1
 
0.1%
12.95891702 1
 
0.1%
Other values (1981) 1981
99.5%
ValueCountFrequency (%)
2.2 1
0.1%
4.371898608 1
0.1%
4.473092264 1
0.1%
4.861631498 1
0.1%
4.966861619 1
0.1%
5.051694615 1
0.1%
5.218232927 1
0.1%
5.315286537 1
0.1%
5.362370906 1
0.1%
5.512039718 1
0.1%
ValueCountFrequency (%)
28.3 1
0.1%
23.95245044 1
0.1%
23.91760126 1
0.1%
23.56964491 1
0.1%
23.51477377 1
0.1%
23.39951606 1
0.1%
23.37326504 1
0.1%
23.31769912 1
0.1%
23.23432591 1
0.1%
23.13595214 1
0.1%

Trihalomethanes
Real number (ℝ)

Distinct1891
Distinct (%)100.0%
Missing100
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean66.816794
Minimum8.1758764
Maximum120.03008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:04.989990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum8.1758764
5-th percentile39.552362
Q156.584099
median66.701621
Q377.766434
95-th percentile92.404823
Maximum120.03008
Range111.8542
Interquartile range (IQR)21.182335

Descriptive statistics

Standard deviation16.307375
Coefficient of variation (CV)0.24406103
Kurtosis0.23289937
Mean66.816794
Median Absolute Deviation (MAD)10.686517
Skewness-0.12898369
Sum126350.56
Variance265.93049
MonotonicityNot monotonic
2023-05-15T15:25:05.130582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.99097046 1
 
0.1%
66.35965777 1
 
0.1%
68.61239104 1
 
0.1%
58.60094012 1
 
0.1%
70.54686225 1
 
0.1%
34.26586026 1
 
0.1%
70.93748133 1
 
0.1%
77.3399182 1
 
0.1%
65.88233542 1
 
0.1%
80.65197814 1
 
0.1%
Other values (1881) 1881
94.5%
(Missing) 100
 
5.0%
ValueCountFrequency (%)
8.175876384 1
0.1%
8.577012933 1
0.1%
16.2915046 1
0.1%
17.00068293 1
0.1%
17.52776496 1
0.1%
17.91572257 1
0.1%
18.10122217 1
0.1%
18.40001219 1
0.1%
19.17517454 1
0.1%
20.33775264 1
0.1%
ValueCountFrequency (%)
120.030077 1
0.1%
118.3572747 1
0.1%
116.1616216 1
0.1%
114.2086714 1
0.1%
113.0488857 1
0.1%
112.622733 1
0.1%
110.7392993 1
0.1%
110.4310803 1
0.1%
108.849568 1
0.1%
108.5894144 1
0.1%

Turbidity
Real number (ℝ)

Distinct1991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9495801
Minimum1.4922066
Maximum6.739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-05-15T15:25:05.286832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.4922066
5-th percentile2.6441244
Q13.4077591
median3.9402823
Q34.4899879
95-th percentile5.2287093
Maximum6.739
Range5.2467934
Interquartile range (IQR)1.0822288

Descriptive statistics

Standard deviation0.78699403
Coefficient of variation (CV)0.19926018
Kurtosis-0.10490908
Mean3.9495801
Median Absolute Deviation (MAD)0.54168975
Skewness0.010736876
Sum7863.614
Variance0.61935961
MonotonicityNot monotonic
2023-05-15T15:25:05.427427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.963135381 1
 
0.1%
2.728799592 1
 
0.1%
3.055790475 1
 
0.1%
4.404132196 1
 
0.1%
4.272202758 1
 
0.1%
5.444927204 1
 
0.1%
4.240031708 1
 
0.1%
4.371747854 1
 
0.1%
2.969433921 1
 
0.1%
4.893750992 1
 
0.1%
Other values (1981) 1981
99.5%
ValueCountFrequency (%)
1.492206615 1
0.1%
1.496100943 1
0.1%
1.659799385 1
0.1%
1.680554025 1
0.1%
1.687624505 1
0.1%
1.81252894 1
0.1%
1.943318777 1
0.1%
1.964863097 1
0.1%
1.986191593 1
0.1%
2.000757032 1
0.1%
ValueCountFrequency (%)
6.739 1
0.1%
6.494249467 1
0.1%
6.389161009 1
0.1%
6.35743852 1
0.1%
6.204846359 1
0.1%
6.073006014 1
0.1%
6.06455925 1
0.1%
6.038184953 1
0.1%
6.032994877 1
0.1%
5.989542791 1
0.1%

Potability
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
0
1250 
1
741 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1250
62.8%
1 741
37.2%

Length

2023-05-15T15:25:05.554651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-15T15:25:05.679621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1250
62.8%
1 741
37.2%

Most occurring characters

ValueCountFrequency (%)
0 1250
62.8%
1 741
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1250
62.8%
1 741
37.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1250
62.8%
1 741
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1250
62.8%
1 741
37.2%

Interactions

2023-05-15T15:24:59.466314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:38.684712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:44.594945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:46.362993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:48.131024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:50.211547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.980799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:53.731623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:55.512904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:57.244365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.028697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:39.680511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.162473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:46.959587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.015121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:50.779549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:52.545972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:54.294702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.084188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:57.827618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.162349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:40.231416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.294517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.093024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.149014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:50.913772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:52.679144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:54.435235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.212291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:57.967708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.296731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:40.795312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.427741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.220311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.276212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.043027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:52.812434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:54.560507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.334308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:58.502715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.429306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:41.310772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.560423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.343867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.425285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.175872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:52.947008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:54.696069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.461184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:58.627864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.560921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:42.060485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.695274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.476037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.545609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.310248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:53.078063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:54.825881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.614343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:58.763219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.696953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:42.580290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.829918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.593796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.690365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.445947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:53.209583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:54.959327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.746213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:58.902571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.845272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:43.079098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:45.962101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.728590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.828119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.579283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:53.342947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:55.106368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.876308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:59.027767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:00.978281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:43.577987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:46.094551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:47.880265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:49.959600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.710586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:53.460655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:55.234139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:56.994283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:59.163035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:25:01.110917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:44.077112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:46.228966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:48.008615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:50.077295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:51.846479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:53.597204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:55.370569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:57.128121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-05-15T15:24:59.298750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2023-05-15T15:25:05.804555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
STATION CODEyearphHardnessSolidsChloraminesSulfateOrganic_carbonTrihalomethanesTurbidityPotability
STATION CODE1.0000.1780.017-0.0400.008-0.0070.023-0.026-0.0120.0100.000
year0.1781.0000.0040.055-0.0080.021-0.033-0.003-0.0310.0210.552
ph0.0170.0041.0000.026-0.1530.0020.1650.0480.031-0.0640.078
Hardness-0.0400.0550.0261.000-0.0590.186-0.027-0.0150.002-0.0240.074
Solids0.008-0.008-0.153-0.0591.000-0.086-0.1260.025-0.0120.0570.037
Chloramines-0.0070.0210.0020.186-0.0861.0000.062-0.0170.008-0.0210.080
Sulfate0.023-0.0330.165-0.027-0.1260.0621.0000.025-0.045-0.0310.203
Organic_carbon-0.026-0.0030.048-0.0150.025-0.0170.0251.000-0.034-0.0000.029
Trihalomethanes-0.012-0.0310.0310.002-0.0120.008-0.045-0.0341.000-0.0450.000
Turbidity0.0100.021-0.064-0.0240.057-0.021-0.031-0.000-0.0451.0000.000
Potability0.0000.5520.0780.0740.0370.0800.2030.0290.0000.0001.000

Missing values

2023-05-15T15:25:01.324949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-15T15:25:01.576610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-15T15:25:01.767651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

STATION CODELOCATIONSSTATEyearphHardnessSolidsChloraminesSulfateOrganic_carbonTrihalomethanesTurbidityPotability
01393DAMANGANGA AT D/S OF MADHUBAN, DAMANDAMAN & DIU2014NaN204.89045520791.3189817.300212368.51644110.37978386.9909702.9631350
11399ZUARI AT D/S OF PT. WHERE KUMBARJRIA CANAL JOINS, GOAGOA20143.716080129.42292118630.0578586.635246NaN15.18001356.3290764.5006560
21475ZUARI AT PANCHAWADIGOA20148.099124224.23625919909.5417329.275884NaN16.86863766.4200933.0559340
33181RIVER ZUARI AT BORIM BRIDGEGOA20148.316766214.37339422018.4174418.059332356.88613618.436524100.3416744.6287710
43182RIVER ZUARI AT MARCAIM JETTYGOA20149.092223181.10150917978.9863396.546600310.13573811.55827931.9979934.0750750
51400MANDOVI AT NEGHBOURHOOD OF PANAJI, GOAGOA20145.584087188.31332428748.6877397.544869326.6783638.39973554.9178622.5597080
61476MANDOVI AT TONCA, MARCELA, GOAGOA201410.223862248.07173528749.7165447.513408393.66339613.78969584.6035562.6729890
73185RIVER MANDOVI AT AMONA BRIDGEGOA20148.635849203.36152313672.0917644.563009303.30977112.36381762.7983094.4014250
83186RIVER MANDOVI AT IFFI JETTYGOA2014NaN118.98857914285.5838547.804174268.64694112.70604953.9288463.5950170
93187RIVER MANDOVI NEAR HOTEL MARRIOTGOA201411.180284227.23146925484.5084919.077200404.04163517.92780671.9766014.3705620
STATION CODELOCATIONSSTATEyearphHardnessSolidsChloraminesSulfateOrganic_carbonTrihalomethanesTurbidityPotability
19811160TAMBIRAPARANI AT CHERANMADEVI,CAUSE WAY,TAMILNADUNAN2003NaN209.75195520214.2165526.045078323.78838320.27899072.7352074.2584891
19821161TAMBIRAPARANI AT TIRUNELVELI,COLLECTORATE, TAMILNADU.NAN20037.046549128.48251730569.8105514.449123281.72471415.14200658.1573042.8692261
19831162TAMBIRAPARANI AT MURAPPANADU, TAMILNADUNAN2003NaN199.84587512635.3677047.886383332.61515413.21753654.5496184.4805741
19841328TAMBIRAPARANI AT PAPPANKULAM,TAMILNADUNAN20037.732880189.50981147022.7458458.226725287.08705314.98005471.2062093.5107281
19851329TAMBIRAPARANI AT RAIL BDG. NR. AMBASAMUDAM, TAMILNADUNAN20036.266800187.82961727577.2136239.141597322.91784813.29025259.4543253.6528451
19861330TAMBIRAPARANI AT ARUMUGANERI, TAMILNADUNAN20036.630252160.92038422557.7795765.305394338.63082815.79310153.2760335.1812021
19871450PALAR AT VANIYAMBADI WATER SUPPLY HEAD WORK, TAMILNADUNAN20036.775631154.37254315525.3939636.084133343.03216117.11854356.1240243.0175441
19881403GUMTI AT U/S SOUTH TRIPURA,TRIPURANAN2003NaN204.73729225680.7173887.980193318.67727320.37683869.0205304.3237851
19891404GUMTI AT D/S SOUTH TRIPURA, TRIPURANAN20038.164992278.34035829045.2611387.992914334.55196617.40662664.2107674.1624961
19901726CHANDRAPUR, AGARTALA D/S OF HAORA RIVER, TRIPURANAN20037.773758251.46284421688.6169436.194910395.08824514.32455267.5843114.0409741